Intelligent searching of electronically stored information

ABSTRACT

Technologies and implementations for training a predictive intelligence associated with electronic discovery (e-discovery) are generally disclosed.

BACKGROUND

Unless otherwise indicated herein, the approaches described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

In general, when litigation has been determined to be reasonably likely,a party may have a duty to preserve material that may be determined tobe relevant to the litigation. The material may be in an electronic form(i.e., electronically stored information). The preserved material mayneed to be reviewed for a variety of potential legal issues before beingexchanged (e.g., during electronic discovery).

SUMMARY

Detailed herein are various methods for training a predictiveintelligence associated with electronic discovery (e-discovery). Examplemethods may include receiving an electronic information index, receivingcriteria to train the predictive intelligence, searching a portion ofthe received electronic information index applicable to the trainedpredictive intelligence, verifying the trained predictive intelligenceusing the searched portion, determining if the verification of thetrained predictive intelligence meets a particular accuracy threshold,and applying the trained predictive intelligence to the receivedelectronic information index upon determining that the verificationmeets the particular accuracy threshold.

The present disclosure also describes various example machine readablenon-transitory storage media having stored therein instructions that,when executed by one or more processors, operatively enable a predictiveintelligence training module to receive an electronic information index,receive criteria to train a predictive intelligence, search a portion ofthe received electronic information index applicable to the trainedpredictive intelligence, verify the trained predictive intelligenceusing the searched portion, determine if the verification of the trainedpredictive intelligence meets a particular accuracy threshold, and applythe trained predictive intelligence to the received electronicinformation index upon determining that the verification meets theparticular accuracy threshold.

The present disclosure also describes various example systems fortraining a predictive intelligence associated with electronic discovery(e-discovery). Example systems may include a processor and a predictiveintelligence training module communicatively coupled to the processor.The predictive intelligence training module including a machine readableno-transitory medium having stored therein instructions that, whenexecuted by the processor, operatively enable the predictiveintelligence training module to receive an electronic information index,receive criteria to train a predictive intelligence, search a portion ofthe received electronic information index applicable to the trainedpredictive intelligence, verify the trained predictive intelligenceusing the searched portion, determine if the verification of the trainedpredictive intelligence meets a particular accuracy threshold, and applythe trained predictive intelligence to the received electronicinformation index upon determining that the verification meets theparticular accuracy threshold.

The foregoing summary is illustrative only and not intended to be in anyway limiting. In addition to the illustrative aspects, embodiments, andfeatures described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description, which are also illustrative only and not intendedto be limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

Subject matter is particularly pointed out and distinctly claimed in theconcluding portion of the specification. The foregoing and otherfeatures of the present disclosure will become more fully apparent fromthe following description and appended claims, taken in conjunction withthe accompanying drawings. Understanding that these drawings depict onlyseveral embodiments in accordance with the disclosure, and aretherefore, not to be considered limiting of its scope. The disclosurewill be described with additional specificity and detail through use ofthe accompanying drawings.

In the drawings:

FIG. 1 illustrates a system in accordance with various embodiments;

FIG. 2 illustrates a block diagram of an example predictive intelligencemodule;

FIG. 3 illustrates a flow chart of an example method for training apredictive intelligence;

FIG. 4 illustrates an example computer program product; and

FIG. 5 illustrates a block diagram of an example computing device, allarranged in accordance with at least some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

The following description sets forth various examples along withspecific details to provide a thorough understanding of the presentdisclosure. The various embodiments may be practiced without some ormore of the specific details disclosed herein. Further, in somecircumstances, well-known methods, procedures, systems, componentsand/or circuits have not been described in detail, for the sake ofbrevity and clarity.

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here. The aspects of the present disclosure, as generallydescribed herein, and illustrated in the Figures, can be arranged,substituted, combined, and designed in a wide variety of differentconfigurations, all of which are explicitly contemplated and made partof this disclosure.

This disclosure is drawn, inter alia, to methods, devices, systems andcomputer readable media related to training a predictive intelligenceassociated with electronic discovery (e-discovery).

In general, as part of electronic discovery (here on out e-discovery), areviewer, usually an attorney, may review numerous documents in anelectronic document set (a corpus) to identify, classify and/orcategorize the documents based at least in part on their content and/orthe context. Manually reviewing, identifying, classifying, and/orcategorizing may be very time consuming and difficult due to the amountof electronic information that may be involved. Using some form ofautomated predictive intelligence for identifying, classifying, and/orcategorizing may provide some assistance to the reviewer.

FIG. 1 illustrates a system in accordance with various embodiments. Thesystem 100 may include a network 102 and one or more client devices 104communicatively coupled to the network 102. Additionally, a serverdevice 106 may be communicatively coupled to the network 102. The serverdevice 106 may also be communicatively coupled to a database 108.Together, the one or more client devices 104, the server device 106, thedatabase 108, and the illustrates a block diagram of an examplee-discovery system 100, arranged in accordance with at least someembodiments of the present disclosure network 102 may make up anelectronic information infrastructure of an organization, whereelectronically stored information (ESI) may reside in various datasources.

In general, predictive intelligence may be a form of machineintelligence. In some examples, predictive intelligence may operate on adata set to determine a representation of the data that may facilitatefurther data processing operations. In one non-limiting example, a setof documents may be indexed such that the index may includerepresentations of one or more of the documents in terms of symbolicrelationships. In some examples, the index may be searched based on thesymbolic relationships to identify symbolically relevant results. Insome examples, predictive intelligence may include feedback providedfrom a human operator to guide and/or improve data processingoperations. In some examples, predictive intelligence may includemachine learning to guide and/or improve data processing operations. Insome examples, the predictive intelligence may be implemented on acomputing device and/or implemented in a module executed by thecomputing device.

In FIG. 1, as part of an e-discovery process, from the ESI, anelectronic information index 110 may be gathered and received by theserver device 106. The electronic information index 110 may be in theform of an indexed ESI. In one example, a reviewer 112 may use randomlysampled portion 114 (e.g., machine random sampling) of the electronicinformation index 110 in order to identify files (e.g., electronicdocuments or records) that may have a probability of being affirmativelyresponsive to some criterion or question associated with the e-discoveryprocess, and the reviewer 112 may label these files as “responsive”.Files that do not have a probability of being affirmatively responsiveto some criterion or questions may be labeled as “non-responsive”.Optionally, the reviewer 112 may not label a file, but instead mark thefile so as not to be used for training a predictive intelligence.

In another example, the reviewer 112 may review the electronicinformation index 110 and may identify files (i.e., a portion 114) thatmay have a probability of being affirmatively responsive to somecriterion or question associated with the e-discovery process, and thereviewer 112 may label these files as “responsive” (i.e., judgmentalsampling). As previously described, files that do not have a probabilityof being affirmatively responsive to some criterion or questions may belabeled as “non-responsive”. Optionally, the reviewer 112 may not labela file, but instead mark the file so as not to be used for training apredictive intelligence.

Once the reviewer 112 has accumulated a threshold amount (i.e., theportion 114) of the electronic information index 110, the reviewer 112may provide the labeled files to a computer (e.g., the server device106). Providing the references to the labeled files to the server device106 may facilitate providing criteria to train a predictive intelligenceassociated with e-discovery, in accordance with various embodiments.That is, training the predictive intelligence may include configuringthe server device 106 to recognize which files may be considered to beresponsive and which files may be considered to be non-responsive, andaccordingly, training the predictive intelligence. The criteria may bestored as rules in the database 108.

Once the predictive intelligence has been trained, a search may beperformed on the portion 114 applicable to the trained predictiveintelligence. In one example, the search on the portion 114 may be usedto verify the trained predictive intelligence, where the trainedpredictive intelligence may review each file, determine a percentprobability of the responsiveness of each file, and display and/or storethe results. The reviewer 112 may review the results and determine ifthe predictive intelligence meets a particular accuracy threshold forthe search. The reviewer 112 may determine the particular accuracythreshold based at least in part on the reviewer's judgment. Forexample, determining if the results of the trained predictiveintelligence is consistent with the determination of the reviewer 112 onthe portion 114 (i.e., follows the criteria). If it is determined thatthe trained predictive intelligence meets the particular accuracythreshold, the predictive intelligence may be applied to the entirecorpus of the ESI (received electronic information 110). The predictiveintelligence may review each file, determine a percent probability ofthe responsiveness of each file, and display and/or store the results.

If it is determined that the trained predictive intelligence does notmeet the particular accuracy threshold, the reviewer 112 may useadditional random and/or judgmental sampling to further train thepredictive intelligence. For example, the reviewer 112 may providefurther criteria based at least in part on additional random and/orjudgmental samplings to the predictive intelligence. The further trainedpredictive intelligence may be verified by applying the further trainedpredictive intelligence to the previous portion 114 and any additionalportions (not shown). Similar to the previously described, if it isdetermined that the further trained predictive intelligence meets theparticular accuracy threshold, the further predictive intelligence maybe applied to the entire corpus of the ESI (received electronicinformation 110). Accordingly, the predictive intelligence may continueto be trained in an iterative manner.

Once the predictive intelligence is applied to the entire corpus of theESI, a labeled corpus may result. A probability of being affirmativelyresponsive to some criterion or question associated with the e-discoveryprocess may be determined and associated with each searched file of thecorpus. Additionally, the searched files may be sorted by relevance. Thesorted files may facilitate determination of files having relevancy thatmay exceed a particular threshold. Files that exceed a particularthreshold may be produced directly to another party or used in someother manner. That is, the predictive intelligence may help predictthese types of files. Alternatively, the sorted files may facilitateprediction of files, which most likely may be responsive.

FIG. 2 illustrates a block diagram of a predictive intelligence trainingsystem 200, arranged in accordance with at least some embodiments of thepresent disclosure. As can be seen in this figure, a computing device202 may include a predictive intelligence training module 204.Additionally, the computing device 202 may be communicatively coupledwith a database 206, a user 208, and a corpus 210. As previouslydescribed, the computing device 202 may receive an electronicinformation index from the corpus 210. Namely, the predictiveintelligence training module 204 may be operatively enabled to receivethe electronic information index. The corpus 210 may bedocuments/records to be indexed related to electronically storedinformation included in an information infrastructure of anorganization. The ESI may be in situ (i.e., may reside on one or moredevices on the network). The computing device may be the server device106 and/or one or more client devices 104. Alternatively, the computingdevice 202 may be ubiquitous (e.g., cloud) based computing device, andaccordingly, the claimed subject matter is not limited in theserespects.

The predictive intelligence training module 204 may receive theelectronic information index. The user 208 may provide the portion 114of the received electronic information index 110 to the computing device202 resulting in criteria being received by the predictive intelligencetraining module 204. The received criteria may be stored in the database204. The received criteria may be used by the predictive intelligencetraining module 204 to train the predictive intelligence.

The predictive intelligence training module 204 may perform a search onthe portion 114 to verify the trained predictive intelligence using thereceived search portion. The predictive intelligence training module 204may determine if the verification of the trained predictive intelligencemeets a particular accuracy threshold. If the verification of thetrained predictive intelligence meets a particular accuracy threshold,the predictive intelligence training module 204 may apply the trainedpredictive intelligence to the corpus 210.

In one example, the predictive intelligence training module 204 mayidentify relevant documents/records and lock them prevent access and/orchanges. Identification of relevant documents may include providing alisting of documents and or a listing of relevant documents. The listingof documents may further include information associated with each listeddocument, such as the locations of the documents, the probability ofbeing affirmatively responsive to some criterion or question associatedwith the e-discovery process of the documents, the locked status of thedocuments, or the like. In some examples, the listing of these documentsmay be sorted, for example sorted by the probability of beingaffirmatively responsive to some criterion or question associated withthe e-discovery process so that the documents indicated as most likelyto be relevant are listed first. The listing of relevant documents maybe saved, printed and/or displayed, or the like.

In another example, the predictive intelligence training module 204 mayreceive further criteria for further train the trained predictiveintelligence, and apply the further trained predictive intelligence tothe corpus 210. As described above with respect to FIG. 1, the furthertraining may be an iterative process.

In one example, the predictive intelligence training module 204 mayreceive electronic information index may be received via a bot that maygo out to the network 102 and find ESI for indexing. The ESI may beincluded as part of an electronic information infrastructure of anorganization.

In another example, the predictive intelligence training module 204 mayreceive criteria related to information being responsive to ane-discovery request.

The corpus 210 may be any type of electronic data such as, but notlimited to a collection of documents, records, images, sounds, files,etc., and accordingly, the claimed subject matter is not limited inthese respects.

The predictive intelligence training module 204 may be implemented inany type of manner in a computing environment such as, but not limitedto, implemented in hardware, software, or any combination thereof.

Even though a user/reviewer may be referred to, it is contemplatedwithin the present disclosure that a machine may also provide criteria.For example, the predictive intelligence training module 204 may receivetraining from another predictive intelligence training module (notshown) that may have had enough iterations to be relatively accurate.That is, a more accurate predictive intelligence training module maytrain a less accurate predictive training module.

Additionally, predictive intelligence may be of a wide variety of formsuch as an application as described with respect to FIG. 5 or any otherform of hardware and/or software.

FIG. 3 illustrates a flow chart of an example method 300 for trainingpredictive intelligence associated with e-discovery, arranged inaccordance with at least some embodiments of the present disclosure.This figure employs block diagrams to illustrate the example methodsdetailed therein. These block diagrams may set out various functionalblocks or actions that may be described as processing steps, functionaloperations, events and/or acts, etc., and may be performed by hardware,software, firmware, and/or combination thereof, and need not necessarilybe performed in the exact order shown. Numerous alternatives oradditions to the functional blocks detailed (and/or combinationsthereof) may be practiced in various implementations. For example,intervening actions not shown in the figures and/or additional actionsnot shown in the figures may be employed and/or some of the actionsshown in the figures may be eliminated. In some examples, the actionsshown in one figure may be operated using techniques discussed withrespect to another figure. Additionally, in some examples, the actionsshown in these figures may be operated using parallel processingtechniques. The above described and other rearrangements, substitutions,changes, modifications, etc., may be made without departing from thescope of claimed subject matter.

Additionally, FIG. 3 is described with reference to elements of thepredictive intelligence training module 200 depicted in FIG. 2. However,the described embodiments are not limited to this depiction. Morespecifically, some elements depicted in FIG. 2 may be omitted fromexample implementations of the methods detailed herein. Additionally,other elements not depicted in FIG. 2 may be used to implement examplemethods.

Turning now to the method 300 and FIG. 3, beginning at block 302,“Receive an Electronic Information Index”, an electronic informationindex may be received from the corpus 210 to train the predictiveintelligence.

From block 302 to block 304, “Receive Criteria to Train the PredictiveIntelligence”, criteria may be received from the user 208 and stored inthe database 206. Alternatively, the criteria may be received from thedatabase 206, the criteria being previously store in the database 206.At block 306, “Searching a Portion of the Received ElectronicInformation Index Applicable to the Trained Predictive Intelligence”,the portion 114 may be searched by the trained predictive intelligencetraining module 204. Moving on to block 308, “Verifying the TrainedPredictive Intelligence using the Searched Portion”, the predictiveintelligence training module 204 may verify the trained predictiveintelligence using the searched portion 114.

From block 308 to block 310, “Determining if the Verification of theTrained Predictive Intelligence Meets a Particular Accuracy Threshold”,the predictive intelligence training module 204 may determine if theverification of the trained predictive intelligence meets a particularaccuracy threshold. As previously described, the particular accuracythreshold may be via the user 208, another predictive intelligencetraining module, a computing device, or any combination thereof.

At block 312, “Applying the Trained Predictive Intelligence to theReceived Electronic Information Index upon Determining that theVerification Meets the Particular Accuracy Threshold”, the predictiveintelligence training module 204 may apply the trained predictiveintelligence to the corpus 210.

In some embodiments, the method described with respect to FIG. 3 andelsewhere herein may be implemented as a computer program product,executable on any suitable computing system, or the like. For example, acomputer program product for facilitating training of a knowledge basefor e-discovery may be provided. Example computer program products aredescribed with respect to FIG. 4 and elsewhere herein.

FIG. 4 illustrates an example computer program product 400, arranged inaccordance with at least some embodiments of the present disclosure. Thecomputer program product 400 may include a machine-readablenon-transitory medium having stored therein a plurality of instructionsthat, when executed (such as by one or more processors), operativelyenable a knowledge base training module to train a knowledge baseassociated with e-discovery according to the embodiments of theprocesses and methods discussed herein. The computer program product 400of one embodiment may include a signal bearing medium 402. The signalbearing medium 402 may include one or more machine-readable instructions404, which, when executed by one or more processors, may operativelyenable a computing device to provide the functionality described herein.In various examples, the devices discussed herein may use some or all ofthe machine-readable instructions.

In some examples, the machine-readable instructions 404 may includeinstructions that, when executed by one or more processors, mayoperatively enable a predictive intelligence training module to receivean electronic information index, receive criteria to train a predictiveintelligence, search a portion of the received electronic informationindex applicable to the trained predictive intelligence, verify thetrained predictive intelligence using the searched portion, determine ifthe verification of the trained predictive intelligence meets aparticular accuracy threshold, and apply the trained predictiveintelligence to the received electronic information index upondetermining that the verification meets the particular accuracythreshold.

In some implementations, signal bearing medium 402 may encompass acomputer-readable medium 406, such as, but not limited to, a hard diskdrive, a Compact Disc (CD), a Digital Versatile Disk (DVD), a digitaltape, memory, etc. In some implementations, the signal bearing medium402 may encompass a recordable medium 408, such as, but not limited to,memory, read/write (R/W) CDs, R/W DVDs, etc. In some implementations,the signal bearing medium 402 may encompass a communications medium 410,such as, but not limited to, a digital and/or an analog communicationmedium (e.g., a fiber optic cable, a waveguide, a wired communicationlink, a wireless communication link, etc.). In some examples, the signalbearing medium 402 may encompass a machine readable non-transitorymedium.

In general, at least some embodiments of the method described withrespect to FIG. 3 and elsewhere herein may be implemented in anysuitable server and/or computing system. Example systems may bedescribed with respect to FIG. 5 and elsewhere herein. In general, thecomputer system may be configured to train a knowledge base associatedwith e-discovery as described herein.

FIG. 5 is a block diagram illustrating an example computing device 500,arranged in accordance with at least some embodiments of the presentdisclosure. In various examples, the computing device 500 may beconfigured to train a knowledge base associated with e-discovery asdiscussed herein. In one example of a configuration 501, the computingdevice 500 may include one or more processors 510 and a system memory520. A memory bus 530 can be used for communicating between the one ormore processors 510 and the system memory 520.

Depending on the particular configuration, the one or more processors510 may be of any type including but not limited to a microprocessor(μP), a microcontroller (μC), a digital signal processor (DSP), or anycombination thereof. The one or more processors 510 may include one ormore levels of caching, such as a level one cache 511 and a level twocache 512, a processor core 513, and registers 514. The processor core513 can include an arithmetic logic unit (ALU), a floating point unit(FPU), a digital signal processing core (DSP Core), or any combinationthereof. A memory controller 515 can also be used with the one or moreprocessors 510, or in some implementations the memory controller 515 canbe an internal part of the processor 510.

Depending on the particular configuration, the system memory 520 may beof any type including but not limited to volatile memory (such as RAM),non-volatile memory (such as ROM, flash memory, etc.) or any combinationthereof. The system memory 520 may include an operating system 521, oneor more applications 522, and program data 524. The one or moreapplications 522 may include a predictive intelligence trainingapplication 523 that can be arranged to perform the functions, actions,and/or operations as described herein including the functional blocks,actions, and/or operations described herein. The program data 524 mayinclude criteria data 525 for use with the predictive intelligencetraining application 523. In some example embodiments, the one or moreapplications 522 may be arranged to operate with the program data 524 onthe operating system 521. This described configuration 501 isillustrated in FIG. 5 by those components within dashed line.

The computing device 500 may have additional features or functionality,and additional interfaces to facilitate communications between theconfiguration 501 and any other devices and interfaces. For example, abus/interface controller 540 may be used to facilitate communicationsbetween the configuration 501 and one or more data storage devices 550via a storage interface bus 541. The one or more data storage devices550 may be removable storage devices 551, non-removable storage devices552, or a combination thereof. Examples of removable storage andnon-removable storage devices include magnetic disk devices such asflexible disk drives and hard-disk drives (HDD), optical disk drivessuch as compact disk (CD) drives or digital versatile disk (DVD) drives,solid state drives (SSD), and tape drives to name a few. Examplecomputer storage media may include volatile and nonvolatile, removableand non-removable media implemented in any method or technology forstorage of information, such as computer readable instructions, datastructures, program modules, or other data.

The system memory 520, the removable storage 551 and the non-removablestorage 552 are all examples of computer storage media. The computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other mediumwhich may be used to store information and which may be accessed by thecomputing device 500. Any such computer storage media may be part of thecomputing device 500.

The computing device 500 may also include an interface bus 542 forfacilitating communication from various interface devices (e.g., outputinterfaces, peripheral interfaces, and communication interfaces) to theconfiguration 501 via the bus/interface controller 540. Example outputinterfaces 560 may include a graphics processing unit 561 and an audioprocessing unit 562, which may be configured to communicate to variousexternal devices such as a display or speakers via one or more A/V ports563. Example peripheral interfaces 570 may include a serial interfacecontroller 571 or a parallel interface controller 572, which may beconfigured to communicate with external devices such as input devices(e.g., keyboard, mouse, pen, voice input device, touch input device,etc.) or other peripheral devices (e.g., printer, scanner, etc.) via oneor more I/O ports 573. An example communication interface 580 includes anetwork controller 581, which may be arranged to facilitatecommunications with one or more other computing devices 583 over anetwork communication via one or more communication ports 582. Acommunication connection is one example of a communication media. Thecommunication media may typically be embodied by computer readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. A “modulateddata signal” may be a signal that has one or more of its characteristicsset or changed in such a manner as to encode information in the signal.By way of example, and not limitation, communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, radio frequency (RF), infrared (IR) andother wireless media. The term computer readable media as used hereinmay include both storage media and communication media.

The computing device 500 may be implemented as a portion of a small-formfactor portable (or mobile) electronic device such as a cell phone, amobile phone, a tablet device, a laptop computer, a personal dataassistant (PDA), a personal media player device, a wireless web-watchdevice, a personal headset device, an application specific device, or ahybrid device that includes any of the above functions. The computingdevice 500 may also be implemented as a personal computer including bothlaptop computer and non-laptop computer configurations. In addition, thecomputing device 500 may be implemented as part of a wireless basestation or other wireless system or device.

Some portions of the foregoing detailed description are presented interms of algorithms or symbolic representations of operations on databits or binary digital signals stored within a computing system memory,such as a computer memory. An algorithm is here, and generally,considered to be a self-consistent sequence of operations or similarprocessing leading to a particular result. In this context, operationsor processing involve physical manipulation of physical quantities.Typically, although not necessarily, such quantities may take the formof electrical or magnetic signals capable of being stored, transferred,combined, compared or otherwise manipulated. It has proven convenient attimes, principally for reasons of common usage, to refer to such signalsas bits, data, values, elements, symbols, characters, terms, numbers,numerals or the like. However, all of these and similar terms are to beassociated with appropriate physical quantities and are merelyconvenient labels. Unless specifically stated otherwise, discussionsutilizing terms such as “processing,” “computing,” “calculating,”“determining” or the like refer to actions or processes of a computingdevice, that manipulates or transforms data represented as physicalelectronic or magnetic quantities within memories, registers, or otherinformation storage devices, transmission devices, or display devices ofthe computing device.

The claimed subject matter is not limited in scope to the particularimplementations described herein. For example, some implementations maybe in hardware, such as employed to operate on a device or combinationof devices, for example, whereas other implementations may be insoftware and/or firmware. Likewise, although claimed subject matter isnot limited in scope in this respect, some implementations may includeone or more articles, such as a signal bearing medium, a storage mediumand/or storage media. This storage media, such as CD-ROMs, computerdisks, flash memory, or the like, for example, may have instructionsstored thereon, that, when executed by a computing device, such as acomputing system, computing platform, or other system, for example, mayresult in execution of a processor in accordance with the claimedsubject matter, such as one of the implementations previously described,for example. As one possibility, a computing device may include one ormore processing units or processors, one or more input/output devices,such as a display, a keyboard and/or a mouse, and one or more memories,such as static random access memory, dynamic random access memory, flashmemory, and/or a hard drive.

There is little distinction left between hardware and softwareimplementations of aspects of systems; the use of hardware or softwareis generally (but not always, in that in certain contexts the choicebetween hardware and software can become significant) a design choicerepresenting cost vs. efficiency tradeoffs. There are various vehiclesby which processes and/or systems and/or other technologies describedherein can be affected (e.g., hardware, software, and/or firmware), andthat the particular vehicle for implementation will vary with thecontext in which the processes and/or systems and/or other technologiesare deployed. For example, if an implementer determines that speed andaccuracy are paramount, the implementer may opt for a mainly hardwareand/or firmware vehicle; if flexibility is paramount, the implementermay opt for a mainly software implementation; or, yet againalternatively, the implementer may opt for some combination of hardware,software, and/or firmware.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, each functionand/or operation within such block diagrams, flowcharts, or examples canbe implemented, individually and/or collectively, by a wide range ofhardware, software, firmware, or virtually any combination thereof. Inone embodiment, several portions of the subject matter described hereinmay be implemented via Application Specific Integrated Circuits (ASICs),Field Programmable Gate Arrays (FPGAs), digital signal processors(DSPs), or other integrated formats. However, some aspects of theembodiments disclosed herein, in whole or in part, can be equivalentlyimplemented in integrated circuits, as one or more computer programsrunning on one or more computers (e.g., as one or more programs runningon one or more computer systems), as one or more programs running on oneor more processors (e.g., as one or more programs running on one or moremicroprocessors), as firmware, or as virtually any combination thereof,and that designing the circuitry and/or writing the code for thesoftware and or firmware is possible in light of this disclosure. Inaddition, the mechanisms of the subject matter described herein arecapable of being distributed as a program product in a variety of forms,and that an illustrative embodiment of the subject matter describedherein applies regardless of the particular type of signal bearingmedium used to actually carry out the distribution. Examples of a signalbearing medium include, but are not limited to, the following: arecordable type medium such as a flexible disk, a hard disk drive (HDD),a Compact Disc (CD), a Digital Versatile Disk (DVD), a digital tape, acomputer memory, etc.; and a transmission type medium such as a digitaland/or an analog communication medium (e.g., a fiber optic cable, awaveguide, a wired communications link, a wireless communication link,etc.).

The devices and/or processes are described in the manner set forthherein, and thereafter engineering practices may be used to integratesuch described devices and/or processes into data processing systems.That is, at least a portion of the devices and/or processes describedherein can be integrated into a data processing system via a reasonableamount of experimentation. A typical data processing system generallyincludes one or more of a system unit housing, a video display device, amemory such as volatile and non-volatile memory, processors such asmicroprocessors and digital signal processors, computational entitiessuch as operating systems, drivers, graphical user interfaces, andapplications programs, one or more interaction devices, such as a touchpad or screen, and/or control systems including feedback loops andcontrol motors (e.g., feedback for sensing position and/or velocity;control motors for moving and/or adjusting components and/orquantities). A typical data processing system may be implementedutilizing any suitable commercially available components, such as thosetypically found in data computing/communication and/or networkcomputing/communication systems.

The subject matter described herein sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. Such depicted architectures are merely exemplary, and thatin fact many other architectures can be implemented which achieve thesame functionality. In a conceptual sense, any arrangement of componentsto achieve the same functionality is effectively “associated” such thatthe particular functionality is achieved. Hence, any two componentsherein combined to achieve a particular functionality can be seen as“associated with” each other such that the particular functionality isachieved, irrespective of architectures or intermedial components.Likewise, any two components so associated can also be viewed as being“operably connected”, or “operably coupled”, to each other to achievethe particular functionality, and any two components capable of being soassociated can also be viewed as being “operably couplable”, to eachother to achieve the particular functionality. Specific examples ofoperably couplable include but are not limited to physically mateableand/or physically interacting components and/or wirelessly interactableand/or wirelessly interacting components and/or logically interactingand/or logically interactable components.

With respect to the use of substantially any plural and/or singularterms herein, the terms may be translated from the plural to thesingular and/or from the singular to the plural as is appropriate to thecontext and/or application. The various singular/plural permutations maybe expressly set forth herein for sake of clarity.

In general, terms used herein, and especially in the appended claims(e.g., bodies of the appended claims) are generally intended as “open”terms (e.g., the term “including” should be interpreted as “includingbut not limited to,” the term “having” should be interpreted as “havingat least,” the term “includes” should be interpreted as “includes but isnot limited to,” etc.). If a specific number of an introduced claimrecitation is intended, such an intent will be explicitly recited in theclaim, and in the absence of such recitation no such intent is present.For example, as an aid to understanding, the following appended claimsmay contain usage of the introductory phrases “at least one” and “one ormore” to introduce claim recitations. However, the use of such phrasesshould not be construed to imply that the introduction of a claimrecitation by the indefinite articles “a” or “an” limits any particularclaim containing such introduced claim recitation to subject mattercontaining only one such recitation, even when the same claim includesthe introductory phrases “one or more” or “at least one” and indefinitearticles such as “a” or “an” (e.g., “a” and/or “an” should typically beinterpreted to mean “at least one” or “one or more”); the same holdstrue for the use of definite articles used to introduce claimrecitations. In addition, even if a specific number of an introducedclaim recitation is explicitly recited, such recitation should typicallybe interpreted to mean at least the recited number (e.g., the barerecitation of “two recitations,” without other modifiers, typicallymeans at least two recitations, or two or more recitations).Furthermore, in those instances where a convention analogous to “atleast one of A, B, and C, etc.” is used, in general such a constructionis intended in the sense generally understood for the convention (e.g.,“a system having at least one of A, B, and C” would include but not belimited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, etc.). In those instances where a convention analogous to “atleast one of A, B, or C, etc.” is used, in general such a constructionis intended in the sense generally understood for the convention (e.g.,“a system having at least one of A, B, or C” would include but not belimited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, etc.). Virtually any disjunctive word and/or phrase presentingtwo or more alternative terms, whether in the description, claims, ordrawings, should be understood to contemplate the possibilities ofincluding one of the terms, either of the terms, or both terms. Forexample, the phrase “A or B” will be understood to include thepossibilities of “A” or “B” or “A and B.”

Reference in the specification to “an implementation,” “oneimplementation,” “some implementations,” or “other implementations” maymean that a particular feature, structure, or characteristic describedin connection with one or more implementations may be included in atleast some implementations, but not necessarily in all implementations.The various appearances of “an implementation,” “one implementation,” or“some implementations” in the preceding description are not necessarilyall referring to the same implementations. Additionally, all of theabove also applies with respect to the various usages of the terms“embodiment” or “embodiments.”

While certain exemplary techniques have been described and shown hereinusing various methods and systems, various other modifications may bemade, and equivalents may be substituted. Additionally, manymodifications may be made to adapt a particular situation to theteachings of claimed subject matter without departing from theconcept(s) described herein. Therefore, it is intended that claimedsubject matter not be limited to the particular examples disclosed, butthat such claimed subject matter also may include all implementationsfalling within the scope of the appended claims, and equivalentsthereof.

What is claimed is/are:
 1. A method for training a predictiveintelligence associated with electronic discovery (e-discovery), themethod comprising: receiving an electronic information index; receivingcriteria to train the predictive intelligence; searching a portion ofthe received electronic information index applicable to the trainedpredictive intelligence; verifying the trained predictive intelligenceusing the searched portion; determining if the verification of thetrained predictive intelligence meets a particular accuracy threshold;and applying the trained predictive intelligence to the receivedelectronic information index upon determining that the verificationmeets the particular accuracy threshold.
 2. The method of claim 1further comprising: identifying relevant documents; and locking therelevant documents.
 3. The method of claim 1 further comprising:receiving further criteria to further train the trained predictiveintelligence; and applying the further trained predictive intelligenceto the received electronic information index.
 4. The method of claim 1,wherein receiving the electronic information index comprises receivingan electronic information index of electronically stored information(ESI) included in an electronic information infrastructure of anorganization in situ.
 5. The method of claim 1, wherein receiving theelectronic information index comprises receiving the indexed electronicinformation via a bot.
 6. The method of claim 1, wherein receiving thecriteria comprises receiving criteria related to information beingresponsive to an e-discovery process.
 7. A machine readablenon-transitory medium having stored therein instructions that, whenexecuted by one or more processors, operatively enable a predictiveintelligence training module to: receive an electronic informationindex; receive criteria to train a predictive intelligence; search aportion of the received electronic information index applicable to thetrained predictive intelligence; verify the trained predictiveintelligence using the searched portion; determine if the verificationof the trained predictive intelligence meets a particular accuracythreshold; and apply the trained predictive intelligence to the receivedelectronic information index upon determining that the verificationmeets the particular accuracy threshold.
 8. The machine readablenon-transitory medium of claim 7, wherein the stored instructions thatoperatively enable the predictive intelligence training module furthercomprise instructions that, when executed by one or more processors,operatively enable the predictive intelligence training module to:identify relevant documents; and lock the relevant documents.
 9. Themachine readable non-transitory medium of claim 7, wherein the storedinstructions that operatively enable the predictive intelligencetraining module further comprise instructions that, when executed by oneor more processors, operatively enable the predictive intelligencetraining module to: receive further criteria to further train thetrained predictive intelligence; and apply the further trainedpredictive intelligence to the received electronic information index.10. The machine readable non-transitory medium of claim 7, wherein thestored instructions that operatively enable the predictive intelligencetraining module to receive an electronic information index includeinstructions that, when executed by one or more processors, operativelyenable the predictive intelligence training module to: receive anelectronic information index of electronically stored information (ESI)included in an electronic information infrastructure of an organizationin situ.
 11. The machine readable non-transitory medium of claim 7,wherein receiving the electronic information index comprises receivingthe electronic information index via a bot.
 12. The machine readablenon-transitory medium of claim 7, wherein receiving the criteriacomprises receiving criteria related to information being responsive toan e-discovery process.
 13. A system for training a predictiveintelligence associated with electronic discovery (e-discovery)comprising: a processor; and a predictive intelligence training modulecommunicatively coupled to the processor, the predictive intelligencetraining module including a machine readable no-transitory medium havingstored therein instructions that, when executed by processor,operatively enable the predictive intelligence training module to:receive an electronic information index; receive criteria to train apredictive intelligence; search a portion of the received electronicinformation index applicable to the trained predictive intelligence;verify the trained predictive intelligence using the searched portion;determine if the verification of the trained predictive intelligencemeets a particular accuracy threshold; and apply the trained predictiveintelligence to the received electronic information index upondetermining that the verification meets the particular accuracythreshold.
 14. The system of claim 13, wherein the stored instructionsthat operatively enable the predictive intelligence training modulefurther comprise instructions that, when executed by the processor,operatively enable the predictive intelligence training module to:identify relevant documents; and lock the relevant documents.
 15. Thesystem of claim 13, wherein the stored instructions that operativelyenable the predictive intelligence training module further compriseinstructions that, when executed by the processor, operatively enablethe predictive intelligence training module to: receive further criteriato further train the trained predictive intelligence; and apply thefurther trained predictive intelligence to the received electronicinformation index.
 16. The system of claim 13, wherein the storedinstructions that operatively enable the predictive intelligencetraining module to receive an electronic information index includeinstructions that, when executed by the processor, operatively enablethe predictive intelligence training module to: receive an electronicinformation index of electronically stored information (ESI) included inan electronic information infrastructure of an organization in situ. 17.The system of claim 13, wherein receiving the electronic informationindex comprises receiving the electronic information index via a bot.18. The system of claim 13, wherein receiving the criteria comprisesreceiving criteria related to information being responsive to ane-discovery process.